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-rw-r--r--R/saemix.R9
-rw-r--r--R/summary.saem.mmkin.R39
2 files changed, 31 insertions, 17 deletions
diff --git a/R/saemix.R b/R/saemix.R
index 1b373078..ee68d202 100644
--- a/R/saemix.R
+++ b/R/saemix.R
@@ -44,6 +44,10 @@
#' # functions from saemix
#' library(saemix)
#' compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
+#' plot(f_saem_fomc$so, plot.type = "convergence")
+#' plot(f_saem_fomc$so, plot.type = "individual.fit")
+#' plot(f_saem_fomc$so, plot.type = "npde")
+#' plot(f_saem_fomc$so, plot.type = "vpc")
#'
#' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
#' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
@@ -64,11 +68,12 @@
#' # analytical solutions written for saemix. When using the analytical
#' # solutions written for mkin this took around four minutes
#' f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
-#' f_saem_dfop_sfo <- saem(f_mmkin["SFO-SFO", ])
+#' f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+#' summary(f_saem_dfop_sfo, data = FALSE)
#'
#' # Using a single core, the following takes about 6 minutes, using 10 cores
#' # it is slower instead of faster
-#' f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
+#' #f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
#' }
#' @export
saem <- function(object, control, ...) UseMethod("saem")
diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R
index 4c9776a6..a8917144 100644
--- a/R/summary.saem.mmkin.R
+++ b/R/summary.saem.mmkin.R
@@ -34,25 +34,34 @@
#' @author Johannes Ranke for the mkin specific parts
#' saemix authors for the parts inherited from saemix.
#' @examples
-#' # Generate five datasets following SFO kinetics
+#' # Generate five datasets following DFOP-SFO kinetics
#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-#' dt50_sfo_in_pop <- 50
-#' k_in_pop <- log(2) / dt50_sfo_in_pop
+#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"),
+#' m1 = mkinsub("SFO"), quiet = TRUE)
#' set.seed(1234)
-#' k_in <- rlnorm(5, log(k_in_pop), 0.5)
-#' SFO <- mkinmod(parent = mkinsub("SFO"))
+#' k1_in <- rlnorm(5, log(0.1), 0.3)
+#' k2_in <- rlnorm(5, log(0.02), 0.3)
+#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3))
+#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3))
+#' k_m1_in <- rlnorm(5, log(0.02), 0.3)
#'
-#' pred_sfo <- function(k) {
-#' mkinpredict(SFO,
-#' c(k_parent = k),
-#' c(parent = 100),
+#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) {
+#' mkinpredict(dfop_sfo,
+#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1),
+#' c(parent = 100, m1 = 0),
#' sampling_times)
#' }
#'
-#' ds_sfo_mean <- lapply(k_in, pred_sfo)
-#' names(ds_sfo_mean) <- paste("ds", 1:5)
+#' ds_mean_dfop_sfo <- lapply(1:5, function(i) {
+#' mkinpredict(dfop_sfo,
+#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i],
+#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]),
+#' c(parent = 100, m1 = 0),
+#' sampling_times)
+#' })
+#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5)
#'
-#' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) {
+#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) {
#' add_err(ds,
#' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2),
#' n = 1)[[1]]
@@ -60,9 +69,9 @@
#'
#' \dontrun{
#' # Evaluate using mmkin and saem
-#' f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)
-#' f_saem <- saem(f_mmkin)
-#' summary(f_saem, data = TRUE)
+#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, quiet = TRUE, error_model = "tc", cores = 5)
+#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo)
+#' summary(f_saem_dfop_sfo, data = TRUE)
#' }
#'
#' @export

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